AI Roadmap Prioritisation: How to Decide What to Build Next
Impact/feasibility for AI — why standard prioritisation frameworks break. How to score AI initiatives accounting for data readiness, model risk, and iteration speed.
Every AI team has more ideas than capacity. The question is never 'what could we build?' — it's 'what should we build first, and why?' That answer requires a framework that isn't just 'highest impact' (everything is highest impact when you're pitching it) but a structured way to compare options across multiple dimensions.
The four-quadrant framework
Before any scoring framework: map every candidate initiative on two axes — value to the user/business (low to high) and feasibility with today's AI capabilities (low to high). The quadrant you care about most is high-value, high-feasibility: these are your immediate priorities. High-value, low-feasibility items go on your 'when the technology matures' list. Low-value, high-feasibility items are traps — easy to build, tempting, but won't move the needle.
The six scoring dimensions
| Dimension | What to ask | Weight |
|---|---|---|
| User value | How much does this reduce pain or add delight for real users? | High |
| Business impact | Revenue, cost savings, retention, or strategic positioning? | High |
| AI feasibility | Can today's models do this reliably? What's the eval baseline? | High |
| Technical risk | What's the P(failure) and how bad is a failure? | Medium |
| Build time | Weeks to MVP, weeks to production-ready? | Medium |
| Defensibility | Can a competitor replicate this in 3 months or is there a moat? | Low-Medium |
The AI-specific considerations
Model capability ceiling
Some ideas are great but the current generation of models can't do them reliably. A legal contract analysis feature sounds high-value — but if your eval set shows 40% error rates on ambiguous clauses, it's not production-ready regardless of how valuable it would be if it worked. Feasibility score must be based on eval data, not intuition.
Trust ceiling
High-automation, low-trust-context features fail in deployment even when they work technically. An AI that auto-files expense reports works — but users don't trust it enough to not double-check every one, eliminating the time savings. Factor in user trust readiness, not just technical capability.
The iteration tax
AI features require ongoing maintenance that static features don't: eval set growth, prompt updates, model version testing, hallucination monitoring. A feature that's easy to build may be expensive to maintain. Factor in ongoing operational cost, not just build cost.
The sequencing principle
Within your high-value, high-feasibility quadrant: build trust-building features before automation features. A feature that shows AI-assisted suggestions (human in the loop) before one that acts autonomously. Users need to see the AI work correctly before they're willing to let it work unsupervised. Sequence your roadmap to build trust incrementally.
The best AI roadmaps have a deliberate 'earn the right to automate' arc: Phase 1 shows the AI's reasoning (suggestions, copilot mode). Phase 2 automates with easy override. Phase 3 fully automates. Each phase builds the trust needed for the next.
AI roadmap scoring template →: Score and rank your AI initiatives with the framework in the AI PM module.
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